System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

Paulo Lopes dos Santos, Mohammad T. Freigoun, Cesar A. Martin, Daniel Rivera, Eric B. Hekler, Rodrigo Alvite Romano, Teresa P. Azevedo Perdicoulis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


System identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies.

Original languageEnglish (US)
Article number8587117
Pages (from-to)264-275
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Issue number1
StatePublished - Jan 2020


  • Behavioral interventions
  • behavioral sciences
  • design of experiments
  • parameter estimation
  • system identification
  • systems modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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